Homology to peptide pattern for annotation of carbohydrate-active enzymes and prediction of function

Peter Kamp Busk, Bo Pilgaard, Mateusz Jakub Lezyk, Anne S. Meyer, Lene Lange

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Abstract

Background: Carbohydrate-active enzymes are found in all organisms and participate in key biological processes.These enzymes are classified in 274 families in the CAZy database but the sequence diversity within each family makes it a major task to identify new family members and to provide basis for prediction of enzyme function. A fastand reliable method for de novo annotation of genes encoding carbohydrate-active enzymes is to identify conserved peptides in the curated enzyme families followed by matching of the conserved peptides to the sequence of interestas demonstrated for the glycosyl hydrolase and the lytic polysaccharide monooxygenase families. This approach notonly assigns the enzymes to families but also provides functional prediction of the enzymes with high accuracy.
Results: We identified conserved peptides for all enzyme families in the CAZy database with Peptide Pattern Recognition. The conserved peptides were matched to protein sequence for de novo annotation and functional prediction of carbohydrate-active enzymes with the Hotpep method. Annotation of protein sequences from 12 bacterial and 16 fungal genomes to families with Hotpep had an accuracy of 0.84 (measured as F1-score) compared to semiautomatic annotation by the CAZy database whereas the dbCAN HMM-based method had an accuracy of 0.77 with optimized parameters. Furthermore, Hotpep provided a functional prediction with 86% accuracy for the annotated genes. Hotpep is available as a stand-alone application for MS Windows.
Conclusions: Hotpep is a state-of-the-art method for automatic annotation and functional prediction of carbohydrate-active enzymes.
Original languageEnglish
Article number214
JournalB M C Bioinformatics
Volume18
Number of pages9
ISSN1471-2105
DOIs
Publication statusPublished - 2017

Bibliographical note

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Keywords

  • Carbohydrate-active enzymes
  • Genomics
  • Annotation
  • Software

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